Grokking System Design Fundamentals
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System Design Fundamentals
Introduction to System Design
Load Balancing
Introduction to Load Balancing
Load Balancing Algorithms
Uses of Load Balancing
Load Balancer Types
Stateless vs. Stateful Load Balancing
High Availability and Fault Tolerance
Scalability and Performance
Challenges of Load Balancers
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
Key Characteristics of Distributed Systems
Scalability
Availability
Latency and Performance
Concurrency and Coordination
Monitoring and Observability
Resilience and Error Handling
Fault Tolerance vs. High Availability
Network Essentials
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Caching
Introduction to Caching
Why is Caching Important?
Types of Caching
Cache Replacement Policies
Cache Invalidation
Cache Read Strategies
Cache Coherence and Consistency Models
Caching Challenges
Cache Performance Metrics
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
CAP & PACELC Theorems
Introduction to CAP Theorem
Components of CAP Theorem
Trade-offs in CAP Theorem
Examples of CAP Theorem in Practice
Beyond CAP Theorem
System Design Trade-offs in Interviews
Databases (SQL vs. NoSQL)
Introduction to Databases
SQL Databases
NoSQL Databases
SQL vs. NoSQL
ACID vs BASE Properties
Real-World Examples and Case Studies
SQL Normalization and Denormalization
In-Memory Database vs. On-Disk Database
Data Replication vs. Data Mirroring
Database Federation
Indexes
What are Indexes?
Types of Indexes
Bloom Filters
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Long-Polling vs. WebSockets vs. Server-Sent Events
Difference Between Long-Polling, WebSockets, and Server-Sent Events
Quorum
What is Quorum?
Heartbeat
What is Heartbeat?
Checksum
What is Checksum?
Uses of Checksum
Leader and Follower
What is Leader and Follower Pattern?
Security
What is Security and Privacy?
What is Authentication?
What is Authorization?
Authentication vs. Authorization
OAuth vs. JWT for Authentication
What is Encryption?
What are DDoS Attacks?
Distributed Messaging System
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Batch Processing vs. Stream Processing
XML vs. JSON
Synchronous vs. Asynchronous Communication
Push vs. Pull Notification Systems
Microservices vs. Serverless Architecture
Message Queues vs. Service Bus
Stateful vs. Stateless Architecture
Event-Driven vs. Polling Architecture
Quiz
Popular Messaging Queue Systems

In this section, we will discuss some popular messaging queue systems and provide a brief overview of their key features and use cases. The following messaging queue systems have gained popularity due to their robustness, scalability, and performance:

a. RabbitMQ

RabbitMQ is an open-source message broker that provides support for various messaging patterns, including publish-subscribe, request-reply, and point-to-point communication. Key features of RabbitMQ include:

  • Flexibility: RabbitMQ supports various messaging patterns and protocols.
  • Clustering and high availability: RabbitMQ can be deployed in clustered configurations for fault tolerance and load balancing.
  • Extensibility: RabbitMQ provides a plugin system to extend its functionality, such as adding support for additional protocols.
  • Monitoring and management: RabbitMQ includes built-in tools for monitoring and managing the message broker.

b. Apache Kafka

Apache Kafka is a distributed streaming platform designed for high-throughput, fault-tolerant, and scalable messaging. Kafka is widely used for stream processing, log aggregation, and event-driven architectures. Key features of Apache Kafka include:

  • Distributed architecture: Kafka scales horizontally, allowing it to handle high-throughput and provide fault tolerance.
  • Durability: Kafka stores messages persistently on disk, ensuring data durability and allowing for message replay.
  • Low latency: Kafka is designed for real-time processing and provides low-latency messaging.
  • Stream processing: Kafka includes a stream processing API for building real-time data processing applications.

c. Amazon Simple Queue Service (SQS)

Amazon SQS is a fully managed message queuing service provided by Amazon Web Services (AWS). It enables decoupling of components in a distributed system, ensuring reliable and scalable communication. Key features of Amazon SQS include:

  • Scalability: SQS automatically scales with the number of messages and the number of consumers.
  • Reliability: SQS guarantees at-least-once message delivery and provides visibility timeouts for message processing.
  • Security: SQS integrates with AWS Identity and Access Management (IAM) to control access to queues and messages.
  • Cost-effective: SQS operates on a pay-as-you-go pricing model, making it cost-effective for various workloads.

d. Apache ActiveMQ

Apache ActiveMQ is an open-source, multi-protocol message broker that supports a variety of messaging patterns. Key features of Apache ActiveMQ include:

  • High availability: ActiveMQ provides support for primary-replica replication and network of brokers for increased availability and load balancing.
  • Message persistence: ActiveMQ supports various persistence options, such as file-based, in-memory, and JDBC-based storage.
  • Integration: ActiveMQ can be easily integrated with various platforms, such as Java EE and Spring.
Messaging patterns
RabbitMQ vs. Kafka vs. ActiveMQ
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On this page

a. RabbitMQ

b. Apache Kafka

c. Amazon Simple Queue Service (SQS)

d. Apache ActiveMQ